Sustainable Aquatic Environments: Sources, Transport, and Fate of Contaminants in Waters and Sediment

Dear Colleagues,

It is our pleasure to announce a new Special Issue, “Sustainable Aquatic Environments: Sources, Transport, and Fate of Contaminants in Waters and Sediment”, of the journal Sustainability.

Water quality underpins ecosystem integrity and public health, biodiversity, habitat condition, sustainable water use, and waterfront livelihoods. To manage these resources responsibly and reduce risk, we must deepen our mechanistic understanding of contaminant transport and reliably predict its spatiotemporal dynamics in surface and ground water systems.

In aquatic environments, contaminant behavior frequently departs from classical Fickian assumptions because of the intricate flow and geometric characteristics of water bodies. Flow-induced shear dispersion, vegetation, and dead zones around in-channel structures generate non-Fickian transport. In particular, hyporheic exchange across the water–sediment interface delays the residence times of contaminants. Consequently, hydrogeologic factors control contaminant transport mechanisms, spanning toxic compounds, metals, microplastics, and suspended sediment.

Beyond in-channel processes, effective management requires multi-scale diagnostics of contaminant sources and drivers from the watershed to the reach scales. Advances in source apportionment, inverse modeling, and tracer-aided hydrology now enable the quantitative prediction and causal attribution of contaminant loads that feed eutrophication, harmful algal blooms (HABs), and sediment-associated transport.

Rapid progress in hyperspectral and multispectral remote sensing combined with in situ sensor networks has enabled the synoptic monitoring of chlorophyll-a, cyanobacteria, and suspended sediments. Fusing these data with process-based models and physics-informed machine learning (ML) enables forecasting and data assimilation, while delivering fast surrogates for scenario testing and parameter inversion. This integration yields reliable forecasts, calibrated state estimates, and quantified uncertainties for diverse water quality problems.

This Special Issue welcomes studies that develop, validate, and apply state-of-the-art frameworks for contaminant source, transport, and fate, integrating process-based models, data assimilation, and physics-informed ML to quantify water quality risk and guide management. We invite original research articles and critical reviews on topics related (but not limited) to the following:

• Eutrophication and harmful algal blooms;
• Microplastics and suspended sediment;
• Multi-scale diagnostics of contaminant sources and drivers;
• Shear dispersion and non-Fickian transport in surface and ground waters;
• Hyperspectral remote sensing and fusion with in situ sensor networks;
• Physics-informed ML for prediction and assimilation.

Dr. Jun Song Kim
Guest Editor

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